tf.nn.rnn_cell.LSTMCell.__call__()

tf.nn.rnn_cell.LSTMCell.__call__(inputs, state, scope=None)

Run one step of LSTM.

Args:

• inputs: input Tensor, 2D, batch x num_units.
• state: if state_is_tuple is False, this must be a state Tensor, 2-D, batch x state_size. If state_is_tuple is True, this must be a tuple of state Tensors, both 2-D, with column sizes c_state and m_state.
• scope: VariableScope for the created subgraph; defaults to "LSTMCell".

Returns:

A tuple containing: - A 2-D, [batch x output_dim], Tensor representing the output of the LSTM after reading inputs when previous state was state. Here output_dim is: num_proj if num_proj was set, num_units otherwise. - Tensor(s) representing the new state of LSTM after reading inputs when the previous state was state. Same type and shape(s) as state.

Raises:

• ValueError: If input size cannot be inferred from inputs via static shape inference.